IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v534y2019ics0378437119311033.html
   My bibliography  Save this article

Competition games between teams vying for common resources under consensus dynamics on networks

Author

Listed:
  • Lopez-Pina, A.
  • Losada, J.C.
  • Benito, R.M.

Abstract

Dynamics on complex networks and associated games have numerous practical applications for a wide range of fields. The analyses addressed in the literature frequently consider game frameworks defined between the individual nodes within a given network. However, many real situations are related to teams of agents which are external to the network but that compete over the state of the network elements. In this paper we carry out an analytical and numerical analysis of games played between two teams that compete to maximize their benefits from the resources of the same population, whose elements form a network. This population could be a group of voters in an election, a set of potential clients within a given market, or a certain species in an ecosystem, whose state favourable to one of the teams (political opinion, volume of purchases or pollination, for example) must be maximized. The dynamics of the state of each node of the network is given by a consensus function, and the steady state depends on the network structure and the external action of each team. We have found an optimal analytical solution for the team’s actions that maximize its benefit, when the network of connections with the population is fixed and equal for both teams. Additionally, we find analytically the optimal network of connections from the team agents to the population so that the achievable payoff for said optimal action is maximum over all alternative networks. Finally, we consider the case of a game played on subsets of the general population by each of the two competing teams, ultimately leading to a Nash equilibrium.

Suggested Citation

  • Lopez-Pina, A. & Losada, J.C. & Benito, R.M., 2019. "Competition games between teams vying for common resources under consensus dynamics on networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
  • Handle: RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119311033
    DOI: 10.1016/j.physa.2019.121874
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119311033
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2019.121874?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ding, Fei & Liu, Yun & Shen, Bo & Si, Xia-Meng, 2010. "An evolutionary game theory model of binary opinion formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1745-1752.
    2. Chacoma, A. & Mato, G. & Kuperman, M.N., 2018. "Dynamical and topological aspects of consensus formation in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 152-161.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lin, XuXun & Yuan, PengCheng, 2018. "A dynamic parking charge optimal control model under perspective of commuters’ evolutionary game behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1096-1110.
    2. Otto, Ilona M. & Wiedermann, Marc & Cremades, Roger & Donges, Jonathan F. & Auer, Cornelia & Lucht, Wolfgang, 2020. "Human agency in the Anthropocene," Ecological Economics, Elsevier, vol. 167(C).
    3. Balankin, Alexander S. & Martínez Cruz, Miguel Ángel & Martínez, Alfredo Trejo, 2011. "Effect of initial concentration and spatial heterogeneity of active agent distribution on opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3876-3887.
    4. Zhe Zhan & Anjing Fan, 2022. "How to Promote Quality and Equity of Early Childhood Education for Sustainable Development in Undeveloped Rural Areas of China: An Evolutionary Game Study," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    5. Maciel, Marcelo V. & Martins, André C.R., 2020. "Ideologically motivated biases in a multiple issues opinion model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    6. Angelo Antoci & Guido Ferilli & Paolo Russu & Pier Luigi Sacco, 2020. "Rational populists: the social consequences of shared narratives," Journal of Evolutionary Economics, Springer, vol. 30(2), pages 479-506, April.
    7. Jianlei Zhang & Chunyan Zhang & Tianguang Chu & Franz J Weissing, 2014. "Cooperation in Networks Where the Learning Environment Differs from the Interaction Environment," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-8, March.
    8. María Cecilia Gimenez & Luis Reinaudi & Ana Pamela Paz-García & Paulo Marcelo Centres & Antonio José Ramirez-Pastor, 2021. "Opinion evolution in the presence of constant propaganda: homogeneous and localized cases," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(1), pages 1-11, January.
    9. Wang, Tao & Huang, Keke & Cheng, Yuan & Zheng, Xiaoping, 2015. "Understanding herding based on a co-evolutionary model for strategy and game structure," Chaos, Solitons & Fractals, Elsevier, vol. 75(C), pages 84-90.
    10. James Burridge & Yu Gao & Yong Mao, 2017. "Delayed response in the Hawk Dove game," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 90(1), pages 1-6, January.
    11. Wu, Liuyi & Dong, Lijun & Wang, Yi & Zhang, Feng & Lee, Victor E. & Kang, Xiaojun & Liang, Qingzhong, 2018. "Uniform-scale assessment of role minimization in bipartite networks and its application to access control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 381-397.
    12. Salehi, Mostafa & Rabiee, Hamid R. & Jalili, Mahdi, 2010. "Motif structure and cooperation in real-world complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(23), pages 5521-5529.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119311033. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.